Overview

Dataset statistics

Number of variables16
Number of observations60
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.6 KiB
Average record size in memory130.1 B

Variable types

NUM16

Reproduction

Analysis started2020-08-25 00:03:01.273190
Analysis finished2020-08-25 00:03:33.720674
Duration32.45 seconds
Versionpandas-profiling v2.8.0
Command linepandas_profiling --config_file config.yaml [YOUR_FILE.csv]
Download configurationconfig.yaml

Warnings

NOX is highly correlated with HCHigh correlation
HC is highly correlated with NOXHigh correlation
DENS has unique values Unique
target has unique values Unique

Variables

PREC
Real number (ℝ≥0)

Distinct count30
Unique (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.36666666666667
Minimum10.0
Maximum60.0
Zeros0
Zeros (%)0.0%
Memory size608.0 B
2020-08-25T00:03:33.768343image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile14.9
Q132.75
median38
Q343.25
95-th percentile52.05
Maximum60
Range50
Interquartile range (IQR)10.5

Descriptive statistics

Standard deviation9.984677527
Coefficient of variation (CV)0.2672081408
Kurtosis1.283610973
Mean37.36666667
Median Absolute Deviation (MAD)5.5
Skewness-0.8021812079
Sum2242
Variance99.69378531
2020-08-25T00:03:33.871818image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
4258.3%
 
4558.3%
 
3558.3%
 
3658.3%
 
3146.7%
 
4346.7%
 
3035.0%
 
3723.3%
 
4123.3%
 
4423.3%
 
4623.3%
 
3823.3%
 
4023.3%
 
1311.7%
 
4711.7%
 
5311.7%
 
1811.7%
 
5211.7%
 
3311.7%
 
1011.7%
 
1511.7%
 
3411.7%
 
3911.7%
 
1111.7%
 
5011.7%
 
Other values (5)58.3%
 
ValueCountFrequency (%) 
1011.7%
 
1111.7%
 
1311.7%
 
1511.7%
 
1811.7%
 
2511.7%
 
2811.7%
 
3035.0%
 
3146.7%
 
3211.7%
 
ValueCountFrequency (%) 
6011.7%
 
5411.7%
 
5311.7%
 
5211.7%
 
5011.7%
 
4711.7%
 
4623.3%
 
4558.3%
 
4423.3%
 
4346.7%
 

JANT
Real number (ℝ≥0)

Distinct count28
Unique (%)46.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.983333333333334
Minimum12.0
Maximum67.0
Zeros0
Zeros (%)0.0%
Memory size608.0 B
2020-08-25T00:03:33.981674image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile23
Q127
median31.5
Q340
95-th percentile54.05
Maximum67
Range55
Interquartile range (IQR)13

Descriptive statistics

Standard deviation10.16889852
Coefficient of variation (CV)0.299231933
Kurtosis1.087821388
Mean33.98333333
Median Absolute Deviation (MAD)5.5
Skewness0.9607114826
Sum2039
Variance103.4064972
2020-08-25T00:03:34.088372image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
3058.3%
 
2458.3%
 
2746.7%
 
3246.7%
 
2946.7%
 
3346.7%
 
4535.0%
 
2835.0%
 
4035.0%
 
4223.3%
 
2323.3%
 
5523.3%
 
3523.3%
 
3123.3%
 
2623.3%
 
2011.7%
 
4811.7%
 
2511.7%
 
3911.7%
 
3811.7%
 
3411.7%
 
6711.7%
 
5411.7%
 
4611.7%
 
4911.7%
 
Other values (3)35.0%
 
ValueCountFrequency (%) 
1211.7%
 
2011.7%
 
2323.3%
 
2458.3%
 
2511.7%
 
2623.3%
 
2746.7%
 
2835.0%
 
2946.7%
 
3058.3%
 
ValueCountFrequency (%) 
6711.7%
 
5523.3%
 
5411.7%
 
5311.7%
 
4911.7%
 
4811.7%
 
4611.7%
 
4535.0%
 
4223.3%
 
4035.0%
 

JULT
Real number (ℝ≥0)

Distinct count20
Unique (%)33.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean74.58333333333333
Minimum63.0
Maximum85.0
Zeros0
Zeros (%)0.0%
Memory size608.0 B
2020-08-25T00:03:34.198767image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum63
5-th percentile67.95
Q172
median74
Q377.25
95-th percentile82.1
Maximum85
Range22
Interquartile range (IQR)5.25

Descriptive statistics

Standard deviation4.763176787
Coefficient of variation (CV)0.06386382285
Kurtosis0.01094462531
Mean74.58333333
Median Absolute Deviation (MAD)3
Skewness0.1367057709
Sum4475
Variance22.68785311
2020-08-25T00:03:34.295801image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
721118.3%
 
7458.3%
 
7758.3%
 
7146.7%
 
7646.7%
 
7346.7%
 
7935.0%
 
7035.0%
 
7535.0%
 
8135.0%
 
8523.3%
 
6823.3%
 
8023.3%
 
8223.3%
 
7823.3%
 
6311.7%
 
8411.7%
 
6911.7%
 
6711.7%
 
6411.7%
 
ValueCountFrequency (%) 
6311.7%
 
6411.7%
 
6711.7%
 
6823.3%
 
6911.7%
 
7035.0%
 
7146.7%
 
721118.3%
 
7346.7%
 
7458.3%
 
ValueCountFrequency (%) 
8523.3%
 
8411.7%
 
8223.3%
 
8135.0%
 
8023.3%
 
7935.0%
 
7823.3%
 
7758.3%
 
7646.7%
 
7535.0%
 

OVR65
Real number (ℝ≥0)

Distinct count39
Unique (%)65.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.798333334922791
Minimum5.599999904632568
Maximum11.800000190734865
Zeros0
Zeros (%)0.0%
Memory size608.0 B
2020-08-25T00:03:34.402567image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum5.599999905
5-th percentile6.48499999
Q17.674999833
median9
Q39.699999809
95-th percentile11.20499983
Maximum11.80000019
Range6.200000286
Interquartile range (IQR)2.024999976

Descriptive statistics

Standard deviation1.464551972
Coefficient of variation (CV)0.1664578865
Kurtosis-0.6036634091
Mean8.798333335
Median Absolute Deviation (MAD)1.050000191
Skewness-0.03411601878
Sum527.9000001
Variance2.144912477
2020-08-25T00:03:34.517699image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
9.19999980958.3%
 
7.30000019135.0%
 
7.69999980935.0%
 
935.0%
 
9.30000019135.0%
 
9.523.3%
 
723.3%
 
11.3000001923.3%
 
10.8999996223.3%
 
8.80000019123.3%
 
6.523.3%
 
8.30000019123.3%
 
9.69999980923.3%
 
8.19999980923.3%
 
7.09999990511.7%
 
811.7%
 
7.511.7%
 
5.59999990511.7%
 
1011.7%
 
6.09999990511.7%
 
9.60000038111.7%
 
8.60000038111.7%
 
7.59999990511.7%
 
7.40000009511.7%
 
9.10000038111.7%
 
Other values (14)1423.3%
 
ValueCountFrequency (%) 
5.59999990511.7%
 
6.09999990511.7%
 
6.19999980911.7%
 
6.523.3%
 
723.3%
 
7.09999990511.7%
 
7.19999980911.7%
 
7.30000019135.0%
 
7.40000009511.7%
 
7.511.7%
 
ValueCountFrequency (%) 
11.8000001911.7%
 
11.3000001923.3%
 
11.1999998111.7%
 
11.1000003811.7%
 
10.8999996223.3%
 
10.6999998111.7%
 
10.6000003811.7%
 
10.3999996211.7%
 
10.1999998111.7%
 
10.1000003811.7%
 

POPN
Real number (ℝ≥0)

Distinct count35
Unique (%)58.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2631666620572406
Minimum2.9200000762939453
Maximum3.5299999713897705
Zeros0
Zeros (%)0.0%
Memory size608.0 B
2020-08-25T00:03:34.789837image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum2.920000076
5-th percentile2.99000001
Q13.210000038
median3.264999986
Q33.359999895
95-th percentile3.451500046
Maximum3.529999971
Range0.6099998951
Interquartile range (IQR)0.1499998569

Descriptive statistics

Standard deviation0.1352523211
Coefficient of variation (CV)0.04144818059
Kurtosis0.04384526971
Mean3.263166662
Median Absolute Deviation (MAD)0.07999992371
Skewness-0.4893159588
Sum195.7899997
Variance0.01829319036
2020-08-25T00:03:34.895582image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
3.22000002946.7%
 
3.21000003846.7%
 
3.31999993346.7%
 
3.2535.0%
 
3.23000001935.0%
 
3.35999989535.0%
 
3.39000010523.3%
 
2.9900000123.3%
 
3.45000004823.3%
 
3.44000005723.3%
 
3.27999997123.3%
 
3.2599999923.3%
 
3.34999990523.3%
 
3.36999988623.3%
 
3.33999991423.3%
 
3.28999996223.3%
 
3.52999997111.7%
 
3.01999998111.7%
 
2.92000007611.7%
 
2.98000001911.7%
 
3.02999997111.7%
 
3.26999998111.7%
 
3.19000005711.7%
 
3.07999992411.7%
 
3.41000008611.7%
 
Other values (10)1016.7%
 
ValueCountFrequency (%) 
2.92000007611.7%
 
2.98000001911.7%
 
2.9900000123.3%
 
3.01999998111.7%
 
3.02999997111.7%
 
3.07999992411.7%
 
3.09999990511.7%
 
3.10999989511.7%
 
3.14000010511.7%
 
3.15000009511.7%
 
ValueCountFrequency (%) 
3.52999997111.7%
 
3.4900000111.7%
 
3.48000001911.7%
 
3.45000004823.3%
 
3.44000005723.3%
 
3.41000008611.7%
 
3.39000010523.3%
 
3.38000011411.7%
 
3.36999988623.3%
 
3.35999989535.0%
 

EDUC
Real number (ℝ≥0)

Distinct count26
Unique (%)43.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.973333358764648
Minimum9.0
Maximum12.300000190734865
Zeros0
Zeros (%)0.0%
Memory size608.0 B
2020-08-25T00:03:35.006390image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile9.600000381
Q110.39999962
median11.05000019
Q311.5
95-th percentile12.19999981
Maximum12.30000019
Range3.300000191
Interquartile range (IQR)1.100000381

Descriptive statistics

Standard deviation0.8452993919
Coefficient of variation (CV)0.07703214367
Kurtosis-0.7513231474
Mean10.97333336
Median Absolute Deviation (MAD)0.6000003815
Skewness-0.2249284354
Sum658.4000015
Variance0.7145310619
2020-08-25T00:03:35.117765image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
11.10000038610.0%
 
11.39999962610.0%
 
12.1000003858.3%
 
10.6999998146.7%
 
12.1999998146.7%
 
9.60000038135.0%
 
10.523.3%
 
11.523.3%
 
1223.3%
 
10.6000003823.3%
 
10.1000003823.3%
 
10.8000001923.3%
 
10.3999996223.3%
 
10.8999996223.3%
 
11.3000001923.3%
 
9.69999980923.3%
 
10.1999998123.3%
 
1123.3%
 
9.89999961911.7%
 
12.3000001911.7%
 
11.8999996211.7%
 
11.8000001911.7%
 
9.80000019111.7%
 
911.7%
 
9.511.7%
 
ValueCountFrequency (%) 
911.7%
 
9.511.7%
 
9.60000038135.0%
 
9.69999980923.3%
 
9.80000019111.7%
 
9.89999961911.7%
 
10.1000003823.3%
 
10.1999998123.3%
 
10.3000001911.7%
 
10.3999996223.3%
 
ValueCountFrequency (%) 
12.3000001911.7%
 
12.1999998146.7%
 
12.1000003858.3%
 
1223.3%
 
11.8999996211.7%
 
11.8000001911.7%
 
11.523.3%
 
11.39999962610.0%
 
11.3000001923.3%
 
11.10000038610.0%
 

HOUS
Real number (ℝ≥0)

Distinct count53
Unique (%)88.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean80.91333351135253
Minimum66.80000305175781
Maximum90.6999969482422
Zeros0
Zeros (%)0.0%
Memory size608.0 B
2020-08-25T00:03:35.232697image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum66.80000305
5-th percentile71.72500305
Q178.37500191
median81.15000153
Q383.60000038
95-th percentile88.93000145
Maximum90.69999695
Range23.8999939
Interquartile range (IQR)5.224998474

Descriptive statistics

Standard deviation5.141372428
Coefficient of variation (CV)0.06354172056
Kurtosis0.3849187533
Mean80.91333351
Median Absolute Deviation (MAD)2.75
Skewness-0.4170024072
Sum4854.800011
Variance26.43371044
2020-08-25T00:03:35.333241image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
79.9000015335.0%
 
83.1999969523.3%
 
79.523.3%
 
82.523.3%
 
79.8000030523.3%
 
81.523.3%
 
79.1999969511.7%
 
7811.7%
 
83.0999984711.7%
 
81.9000015311.7%
 
77.4000015311.7%
 
85.4000015311.7%
 
86.3000030511.7%
 
78.8000030511.7%
 
66.8000030511.7%
 
8111.7%
 
89.511.7%
 
79.3000030511.7%
 
88.5999984711.7%
 
70.3000030511.7%
 
87.511.7%
 
83.511.7%
 
72.511.7%
 
72.8000030511.7%
 
8711.7%
 
Other values (28)2846.7%
 
ValueCountFrequency (%) 
66.8000030511.7%
 
69.1999969511.7%
 
70.3000030511.7%
 
71.8000030511.7%
 
72.511.7%
 
72.8000030511.7%
 
73.8000030511.7%
 
76.1999969511.7%
 
76.8000030511.7%
 
7711.7%
 
ValueCountFrequency (%) 
90.6999969511.7%
 
90.5999984711.7%
 
89.511.7%
 
88.9000015311.7%
 
88.5999984711.7%
 
87.6999969511.7%
 
87.511.7%
 
87.0999984711.7%
 
8711.7%
 
86.3000030511.7%
 

DENS
Real number (ℝ≥0)

UNIQUE

Distinct count60
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3876.05
Minimum1441.0
Maximum9699.0
Zeros0
Zeros (%)0.0%
Memory size608.0 B
2020-08-25T00:03:35.438962image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1441
5-th percentile1881.05
Q13104.25
median3567
Q34519.75
95-th percentile6448.05
Maximum9699
Range8258
Interquartile range (IQR)1415.5

Descriptive statistics

Standard deviation1454.102361
Coefficient of variation (CV)0.3751505684
Kurtosis3.590993081
Mean3876.05
Median Absolute Deviation (MAD)703.5
Skewness1.37949291
Sum232563
Variance2114413.675
2020-08-25T00:03:35.537758image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
492311.7%
 
421311.7%
 
345111.7%
 
226911.7%
 
332711.7%
 
612211.7%
 
209511.7%
 
425311.7%
 
332511.7%
 
366511.7%
 
435511.7%
 
167111.7%
 
428111.7%
 
188311.7%
 
315211.7%
 
270211.7%
 
516011.7%
 
268211.7%
 
376811.7%
 
470011.7%
 
350811.7%
 
326211.7%
 
317211.7%
 
322611.7%
 
184411.7%
 
Other values (35)3558.3%
 
ValueCountFrequency (%) 
144111.7%
 
167111.7%
 
184411.7%
 
188311.7%
 
209511.7%
 
214011.7%
 
226911.7%
 
230211.7%
 
264711.7%
 
268211.7%
 
ValueCountFrequency (%) 
969911.7%
 
746211.7%
 
658211.7%
 
644111.7%
 
612211.7%
 
609211.7%
 
530811.7%
 
516011.7%
 
492311.7%
 
484311.7%
 

NONW
Real number (ℝ≥0)

Distinct count54
Unique (%)90.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.870000085234642
Minimum0.8000000119209291
Maximum38.5
Zeros0
Zeros (%)0.0%
Memory size608.0 B
2020-08-25T00:03:35.650412image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0.8000000119
5-th percentile2.190000045
Q14.950000048
median10.4000001
Q315.65000033
95-th percentile28.74000034
Maximum38.5
Range37.69999999
Interquartile range (IQR)10.70000029

Descriptive statistics

Standard deviation8.921148038
Coefficient of variation (CV)0.7515710171
Kurtosis0.9359849229
Mean11.87000009
Median Absolute Deviation (MAD)5.400000095
Skewness1.131128349
Sum712.2000051
Variance79.58688231
2020-08-25T00:03:35.760934image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
13.1000003835.0%
 
3.523.3%
 
2123.3%
 
8.80000019123.3%
 
8.10000038123.3%
 
7.511.7%
 
2.70000004811.7%
 
14.8000001911.7%
 
13.6999998111.7%
 
15.6000003811.7%
 
2.90000009511.7%
 
5.30000019111.7%
 
28.6000003811.7%
 
111.7%
 
22.2000007611.7%
 
9.511.7%
 
2.511.7%
 
31.3999996211.7%
 
311.7%
 
511.7%
 
17.511.7%
 
211.7%
 
13.511.7%
 
11.511.7%
 
1311.7%
 
Other values (29)2948.3%
 
ValueCountFrequency (%) 
0.800000011911.7%
 
111.7%
 
211.7%
 
2.20000004811.7%
 
2.511.7%
 
2.70000004811.7%
 
2.90000009511.7%
 
311.7%
 
3.40000009511.7%
 
3.523.3%
 
ValueCountFrequency (%) 
38.511.7%
 
36.7000007611.7%
 
31.3999996211.7%
 
28.6000003811.7%
 
27.1000003811.7%
 
25.8999996211.7%
 
24.3999996211.7%
 
22.7000007611.7%
 
22.2000007611.7%
 
2123.3%
 

WWDRK
Real number (ℝ≥0)

Distinct count51
Unique (%)85.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.08166662851969
Minimum33.799999237060554
Maximum59.70000076293945
Zeros0
Zeros (%)0.0%
Memory size608.0 B
2020-08-25T00:03:35.881454image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum33.79999924
5-th percentile38.7899992
Q143.24999905
median45.5
Q349.52499962
95-th percentile51.96000137
Maximum59.70000076
Range25.90000153
Interquartile range (IQR)6.275000572

Descriptive statistics

Standard deviation4.613043144
Coefficient of variation (CV)0.1001058226
Kurtosis0.5548515979
Mean46.08166663
Median Absolute Deviation (MAD)3.149999619
Skewness0.09845541195
Sum2764.899998
Variance21.28016705
2020-08-25T00:03:35.983746image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
45.2999992423.3%
 
4423.3%
 
45.523.3%
 
51.5999984723.3%
 
43.7000007623.3%
 
51.9000015323.3%
 
51.2000007623.3%
 
47.2999992423.3%
 
42.5999984723.3%
 
42.2000007611.7%
 
41.4000015311.7%
 
38.7999992411.7%
 
54.2999992411.7%
 
45.7000007611.7%
 
33.7999992411.7%
 
45.2000007611.7%
 
40.4000015311.7%
 
39.4000015311.7%
 
48.7000007611.7%
 
44.9000015311.7%
 
37.511.7%
 
50.511.7%
 
5111.7%
 
49.511.7%
 
43.511.7%
 
Other values (26)2643.3%
 
ValueCountFrequency (%) 
33.7999992411.7%
 
37.511.7%
 
38.5999984711.7%
 
38.7999992411.7%
 
39.4000015311.7%
 
40.4000015311.7%
 
4111.7%
 
41.2999992411.7%
 
41.4000015311.7%
 
41.9000015311.7%
 
ValueCountFrequency (%) 
59.7000007611.7%
 
54.2999992411.7%
 
53.0999984711.7%
 
51.9000015323.3%
 
51.5999984723.3%
 
51.2000007623.3%
 
5111.7%
 
50.7000007611.7%
 
50.511.7%
 
50.2999992411.7%
 

POOR
Real number (ℝ≥0)

Distinct count46
Unique (%)76.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.373333358764649
Minimum9.399999618530273
Maximum26.399999618530273
Zeros0
Zeros (%)0.0%
Memory size608.0 B
2020-08-25T00:03:36.090792image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum9.399999619
5-th percentile9.795000172
Q112
median13.19999981
Q315.15000033
95-th percentile24.20000076
Maximum26.39999962
Range17
Interquartile range (IQR)3.150000334

Descriptive statistics

Standard deviation4.160095682
Coefficient of variation (CV)0.2894315172
Kurtosis1.531449492
Mean14.37333336
Median Absolute Deviation (MAD)1.400000095
Skewness1.463524127
Sum862.4000015
Variance17.30639608
2020-08-25T00:03:36.207851image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
13.1999998146.7%
 
1235.0%
 
12.3999996235.0%
 
12.3000001935.0%
 
13.8999996223.3%
 
13.523.3%
 
24.2000007623.3%
 
13.6000003823.3%
 
1423.3%
 
9.511.7%
 
20.6000003811.7%
 
17.511.7%
 
9.39999961911.7%
 
17.7000007611.7%
 
14.511.7%
 
11.6999998111.7%
 
14.3000001911.7%
 
10.8999996211.7%
 
19.511.7%
 
18.511.7%
 
10.511.7%
 
1311.7%
 
24.1000003811.7%
 
15.1000003811.7%
 
15.3000001911.7%
 
Other values (21)2135.0%
 
ValueCountFrequency (%) 
9.39999961911.7%
 
9.511.7%
 
9.69999980911.7%
 
9.80000019111.7%
 
10.1000003811.7%
 
10.3000001911.7%
 
10.511.7%
 
10.6999998111.7%
 
10.8000001911.7%
 
10.8999996211.7%
 
ValueCountFrequency (%) 
26.3999996211.7%
 
25.511.7%
 
24.2000007623.3%
 
24.1000003811.7%
 
22.3999996211.7%
 
20.6000003811.7%
 
19.511.7%
 
18.511.7%
 
17.8999996211.7%
 
17.7000007611.7%
 

HC
Real number (ℝ≥0)

HIGH CORRELATION

Distinct count34
Unique (%)56.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.85
Minimum1.0
Maximum648.0
Zeros0
Zeros (%)0.0%
Memory size608.0 B
2020-08-25T00:03:36.330768image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.95
Q17
median14.5
Q330.25
95-th percentile106.95
Maximum648
Range647
Interquartile range (IQR)23.25

Descriptive statistics

Standard deviation91.97767323
Coefficient of variation (CV)2.430057417
Kurtosis34.68521516
Mean37.85
Median Absolute Deviation (MAD)8.5
Skewness5.593422358
Sum2271
Variance8459.892373
2020-08-25T00:03:36.439466image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
658.3%
 
746.7%
 
846.7%
 
1146.7%
 
535.0%
 
1835.0%
 
2035.0%
 
1223.3%
 
3123.3%
 
123.3%
 
1723.3%
 
1423.3%
 
2123.3%
 
423.3%
 
4511.7%
 
10511.7%
 
31111.7%
 
14411.7%
 
4311.7%
 
3011.7%
 
5611.7%
 
8811.7%
 
2611.7%
 
311.7%
 
1511.7%
 
Other values (9)915.0%
 
ValueCountFrequency (%) 
123.3%
 
311.7%
 
423.3%
 
535.0%
 
658.3%
 
746.7%
 
846.7%
 
1146.7%
 
1223.3%
 
1311.7%
 
ValueCountFrequency (%) 
64811.7%
 
31111.7%
 
14411.7%
 
10511.7%
 
8811.7%
 
6511.7%
 
5611.7%
 
5211.7%
 
4511.7%
 
4311.7%
 

NOX
Real number (ℝ≥0)

HIGH CORRELATION

Distinct count30
Unique (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.65
Minimum1.0
Maximum319.0
Zeros0
Zeros (%)0.0%
Memory size608.0 B
2020-08-25T00:03:36.551514image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.95
Q14
median9
Q323.75
95-th percentile63.15
Maximum319
Range318
Interquartile range (IQR)19.75

Descriptive statistics

Standard deviation46.33328964
Coefficient of variation (CV)2.045619852
Kurtosis30.26372797
Mean22.65
Median Absolute Deviation (MAD)6
Skewness5.165555084
Sum1359
Variance2146.773729
2020-08-25T00:03:36.661141image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
4711.7%
 
758.3%
 
358.3%
 
846.7%
 
3246.7%
 
135.0%
 
1135.0%
 
2623.3%
 
523.3%
 
1523.3%
 
2123.3%
 
223.3%
 
923.3%
 
1211.7%
 
6311.7%
 
3811.7%
 
611.7%
 
1011.7%
 
1311.7%
 
2811.7%
 
31911.7%
 
3711.7%
 
1811.7%
 
2311.7%
 
1411.7%
 
Other values (5)58.3%
 
ValueCountFrequency (%) 
135.0%
 
223.3%
 
358.3%
 
4711.7%
 
523.3%
 
611.7%
 
758.3%
 
846.7%
 
923.3%
 
1011.7%
 
ValueCountFrequency (%) 
31911.7%
 
17111.7%
 
6611.7%
 
6311.7%
 
5911.7%
 
3811.7%
 
3711.7%
 
3511.7%
 
3246.7%
 
2811.7%
 

SO2
Real number (ℝ≥0)

Distinct count44
Unique (%)73.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53.766666666666666
Minimum1.0
Maximum278.0
Zeros0
Zeros (%)0.0%
Memory size608.0 B
2020-08-25T00:03:36.770062image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q111
median30
Q369
95-th percentile193.65
Maximum278
Range277
Interquartile range (IQR)58

Descriptive statistics

Standard deviation63.39046784
Coefficient of variation (CV)1.178991962
Kurtosis3.55159773
Mean53.76666667
Median Absolute Deviation (MAD)22
Skewness1.911797878
Sum3226
Variance4018.351412
2020-08-25T00:03:36.875558image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
1610.0%
 
2046.7%
 
1823.3%
 
823.3%
 
1123.3%
 
423.3%
 
1023.3%
 
3323.3%
 
3923.3%
 
2523.3%
 
7211.7%
 
6411.7%
 
12411.7%
 
2811.7%
 
1611.7%
 
1511.7%
 
27811.7%
 
14611.7%
 
6211.7%
 
2711.7%
 
3711.7%
 
2411.7%
 
20611.7%
 
4911.7%
 
511.7%
 
Other values (19)1931.7%
 
ValueCountFrequency (%) 
1610.0%
 
311.7%
 
423.3%
 
511.7%
 
823.3%
 
1023.3%
 
1123.3%
 
1511.7%
 
1611.7%
 
1823.3%
 
ValueCountFrequency (%) 
27811.7%
 
26311.7%
 
20611.7%
 
19311.7%
 
16111.7%
 
14611.7%
 
13011.7%
 
12511.7%
 
12411.7%
 
10811.7%
 

HUMID
Real number (ℝ≥0)

Distinct count17
Unique (%)28.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean57.666666666666664
Minimum38.0
Maximum73.0
Zeros0
Zeros (%)0.0%
Memory size608.0 B
2020-08-25T00:03:36.991844image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum38
5-th percentile52.95
Q155
median57
Q360
95-th percentile71
Maximum73
Range35
Interquartile range (IQR)5

Descriptive statistics

Standard deviation5.36993093
Coefficient of variation (CV)0.09312018954
Kurtosis4.289498267
Mean57.66666667
Median Absolute Deviation (MAD)2.5
Skewness0.2375082056
Sum3460
Variance28.83615819
2020-08-25T00:03:37.104279image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
561118.3%
 
54813.3%
 
58711.7%
 
59610.0%
 
6158.3%
 
6058.3%
 
5746.7%
 
5335.0%
 
7123.3%
 
5523.3%
 
5211.7%
 
7211.7%
 
4711.7%
 
6411.7%
 
6211.7%
 
7311.7%
 
3811.7%
 
ValueCountFrequency (%) 
3811.7%
 
4711.7%
 
5211.7%
 
5335.0%
 
54813.3%
 
5523.3%
 
561118.3%
 
5746.7%
 
58711.7%
 
59610.0%
 
ValueCountFrequency (%) 
7311.7%
 
7211.7%
 
7123.3%
 
6411.7%
 
6211.7%
 
6158.3%
 
6058.3%
 
59610.0%
 
58711.7%
 
5746.7%
 

target
Real number (ℝ≥0)

UNIQUE

Distinct count60
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean940.358432006836
Minimum790.7329711914062
Maximum1113.156005859375
Zeros0
Zeros (%)0.0%
Memory size608.0 B
2020-08-25T00:03:37.211955image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum790.7329712
5-th percentile843.8357788
Q1898.3719788
median943.6830139
Q3983.2057648
95-th percentile1025.745856
Maximum1113.156006
Range322.4230347
Interquartile range (IQR)84.83378601

Descriptive statistics

Standard deviation62.20627601
Coefficient of variation (CV)0.06615166504
Kurtosis0.1633224211
Mean940.358432
Median Absolute Deviation (MAD)44.28652954
Skewness0.09841898094
Sum56421.50592
Variance3869.620776
2020-08-25T00:03:37.324905image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
857.622009311.7%
 
921.869995111.7%
 
1003.50201411.7%
 
1024.8850111.7%
 
861.439025911.7%
 
953.559997611.7%
 
946.184997611.7%
 
967.802978511.7%
 
923.234008811.7%
 
941.181030311.7%
 
844.052978511.7%
 
912.202026411.7%
 
982.291015611.7%
 
968.66497811.7%
 
958.838989311.7%
 
950.671997111.7%
 
874.281005911.7%
 
904.155029311.7%
 
952.528991711.7%
 
861.833007811.7%
 
1015.0230111.7%
 
1071.2889411.7%
 
936.234008811.7%
 
989.265014611.7%
 
991.28997811.7%
 
Other values (35)3558.3%
 
ValueCountFrequency (%) 
790.732971211.7%
 
823.763977111.7%
 
839.708984411.7%
 
844.052978511.7%
 
857.622009311.7%
 
860.101013211.7%
 
861.439025911.7%
 
861.833007811.7%
 
871.338012711.7%
 
871.765991211.7%
 
ValueCountFrequency (%) 
1113.15600611.7%
 
1071.2889411.7%
 
1030.38000511.7%
 
1025.50195311.7%
 
1024.8850111.7%
 
1017.61297611.7%
 
1015.0230111.7%
 
1006.4899911.7%
 
1003.50201411.7%
 
1001.90197811.7%
 

Interactions

2020-08-25T00:03:02.010064image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:02.120683image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:02.230413image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:02.338046image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:02.452928image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:02.566694image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:02.687669image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:02.794099image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:03.057079image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:03.176234image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:03.285881image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:03.400888image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:03.517440image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:03.629969image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:03.738467image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:03.851117image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:03.977456image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:04.099250image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:04.206771image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:04.311993image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:04.424383image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:04.543829image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:04.657886image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:04.768297image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:04.874401image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:04.994424image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:05.114164image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:05.237637image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:05.350403image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:05.462518image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:05.576023image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:05.691087image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:05.811084image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:05.915862image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:06.021329image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:06.122233image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:06.229442image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:06.342982image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:06.615615image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:06.716776image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:06.818533image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:06.937625image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:07.043483image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:07.153629image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:07.261612image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:07.368909image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:07.473372image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:07.583476image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:07.692450image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:07.805957image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:07.920339image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:08.029991image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:08.149361image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:08.268061image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:08.387116image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:08.498458image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:08.613833image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:08.738332image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:08.853292image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:08.972749image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:09.095386image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:09.215112image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:09.329859image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:09.447896image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:09.567963image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:09.685262image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:09.799732image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:10.069463image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:10.189391image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:10.310078image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:10.431234image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:10.543980image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:10.663423image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:10.793963image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:10.911705image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:11.033406image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2020-08-25T00:03:11.520823image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:11.645174image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2020-08-25T00:03:11.988005image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2020-08-25T00:03:12.819643image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:12.940135image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:13.059187image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:13.181539image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:13.305711image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:13.425328image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:13.706930image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2020-08-25T00:03:13.920086image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:14.019718image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2020-08-25T00:03:15.098821image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2020-08-25T00:03:15.846648image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:15.956980image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:16.067621image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:16.171819image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:16.276628image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:16.394831image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:16.504646image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:16.615609image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:16.728019image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:16.837808image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:17.103800image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:17.214586image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:17.325634image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:17.448644image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:17.572361image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:17.697674image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:17.825758image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:17.953598image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:18.082692image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:18.202930image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:18.327134image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:18.460942image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:18.588229image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:18.722798image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:18.849213image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:18.976634image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:19.100924image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:19.229186image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:19.358023image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:19.467842image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:19.577096image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:19.683131image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:19.802543image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:19.925186image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:20.040569image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:20.147728image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:20.257702image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:20.379309image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:20.648900image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:20.765625image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2020-08-25T00:03:20.999006image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:21.108411image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:21.222297image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:21.341066image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:21.458132image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:21.574935image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:21.688931image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:21.810535image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2020-08-25T00:03:22.053796image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2020-08-25T00:03:22.772455image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2020-08-25T00:03:23.126279image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:23.246578image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:23.363063image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:23.480247image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:23.593593image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:23.710016image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:23.833221image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:23.951384image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:24.233233image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:24.345238image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:24.473144image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:24.587403image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:24.711708image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:24.832688image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:24.949131image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:25.060664image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:25.180290image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:25.300323image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:25.415428image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:25.529125image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:25.639509image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:25.755970image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:25.875347image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:25.993577image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:26.103696image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:26.213309image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:26.345289image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:26.464702image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:26.582945image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:26.699518image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:26.818270image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:26.932404image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:27.048664image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:27.166185image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:27.276469image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:27.385789image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:27.490725image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:27.759966image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:27.875250image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:27.988555image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:28.093067image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:28.197270image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:28.316390image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:28.428428image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:28.549626image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:28.665879image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:28.779098image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:28.892799image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:29.006194image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:29.122227image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:29.236089image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:29.349339image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:29.462683image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:29.580165image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:29.698128image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:29.816768image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:29.931262image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:30.044041image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:30.172083image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:30.288087image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:30.413480image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:30.531837image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:30.649600image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:30.770533image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:30.889508image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:31.011466image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:31.288057image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:31.412995image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:31.526416image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:31.648591image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:31.769461image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:31.893016image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:32.012752image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:32.128342image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:32.259181image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:32.377162image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:32.502673image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:32.622907image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:32.748760image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:32.872946image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:32.995052image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Correlations

2020-08-25T00:03:37.465842image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-08-25T00:03:37.898622image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-08-25T00:03:38.171213image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-08-25T00:03:38.440087image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2020-08-25T00:03:33.249660image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:33.596708image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Sample

First rows

PRECJANTJULTOVR65POPNEDUCHOUSDENSNONWWWDRKPOORHCNOXSO2HUMIDtarget
036.027.071.08.13.3411.481.5000003243.08.842.59999811.721.015.059.059.0921.869995
135.023.072.011.13.1411.078.8000034281.03.550.70000114.48.010.039.057.0997.875000
244.029.074.010.43.219.881.5999984260.00.839.40000212.46.06.033.054.0962.354004
347.045.079.06.53.4111.177.5000003125.027.150.20000120.618.08.024.056.0982.291016
443.035.077.07.63.449.684.5999986441.024.443.70000114.343.038.0206.055.01071.288940
553.045.080.07.73.4510.266.8000033325.038.543.09999825.530.032.072.054.01030.380005
643.030.074.010.93.2312.183.9000024679.03.549.20000111.321.032.062.056.0934.700012
745.030.073.09.33.2910.686.0000002140.05.340.40000210.56.04.04.056.0899.528992
836.024.070.09.03.3110.583.1999976582.08.142.50000012.618.012.037.061.01001.901978
936.027.072.09.53.3610.779.3000034213.06.741.00000013.212.07.020.059.0912.346985

Last rows

PRECJANTJULTOVR65POPNEDUCHOUSDENSNONWWWDRKPOORHCNOXSO2HUMIDtarget
5045.028.074.010.63.2111.182.5999981883.03.441.90000212.35.04.020.056.0904.155029
5138.024.072.09.83.3411.478.0000004923.03.850.50000011.18.05.025.061.0950.671997
5231.026.073.09.33.2210.781.3000033249.09.543.90000213.611.07.025.059.0972.463989
5340.023.071.011.33.2810.373.8000031671.02.547.40000213.55.02.011.060.0912.202026
5441.037.078.06.23.2512.389.5000005308.025.959.70000110.365.028.0102.052.0967.802979
5528.032.081.07.03.2712.181.0000003665.07.551.59999813.24.02.01.054.0823.763977
5645.033.076.07.73.3911.382.1999973152.012.147.29999910.914.011.042.056.01003.502014
5745.024.070.011.83.2511.179.8000033678.01.044.79999914.07.03.08.056.0895.695984
5842.033.076.09.73.229.076.1999979699.04.842.20000114.58.08.049.054.0911.817017
5938.028.072.08.93.4810.779.8000033451.011.737.50000013.014.013.039.058.0954.442017